I would like help responding to each Discussion Question Responses. Elaborating/ Opening a discussion on the statement they made regarding the question below:

*Question: When is the analysis of variance (ANOVA) statistical test necessary instead of attest? Compare an ANOVA with attest and provide an example of each.

DQ 1: Danny

When there are more than two treatment conditions for an independent-measures study, the t statistic cannot be used. Instead of testing a hypothesis with a t statistic test, a testing method that can measure multiple factors is needed. The analysis of variance, or ANOVA, is a hypothesis test that can be used for independent measures studies in situations when there are two or more treatment conditions or populations. For example, a t statistic can measure two separate populations of students, one studying for a test with the tv on and one group without tv. A ANOVA test could measure multiple factors, such as, a group studying with no tv on, a group with a loud tv in the background, and a third group with a tv but no sound on.

DQ 2: Ramiah

An analysis of variance statistical test (ANOVA) is a hypothesis-testing procedure that is used to evaluate mean differences between two or more treatments or populations. It is necessary to use instead of a t-test when there are more than two treatments being compared. A t-test is limited to only two tests. An example would be if in a t-test we were comparing how much more successful vaccine A was in reducing covid infections than vaccine B. If other vaccines were now being used and we wanted to know which was most successful out three different vaccines in reducing covid infections, we would then need to use an ANOVA hypothesis testing procedure.

DQ 3: Samuel

In order to know when analysis of variance (ANOVA) statistical test is necessary instead of t tests, we must first learn its definition. Analysis of variance is identified as a hypothesis-testing procedure that is used to evaluate mean differences between two or more populations (Gravetter, Wallnau, Forzano, & Witnaur, 2021). Both t test and ANOVA use sample data to test hypotheses about a given population mean (Gravetter, et al., 2021). The difference is that ANOVA allows the researcher to compare more than two treatments at a time, when the t test is limited to only two comparable treatments. An ANOVA test would be me studying a child, a teenager, and an adult to find out which stage in life do you have the best chance of learning a new language. In a t test, I would test two groups, those under 21 years of age and the other 21 years and older.

DQ4: Delanna

The analysisvariance of the ANOVA is used when necessary when one wants to understand the difference in how different groups respond with the null hypothesis for the test the means of each group difference. An example that would prove the difference and show similarities is that when conducting test both prove to be effective but it is preferred to use a ANOVA than a t test would using more than one groups. An example a t test is that imagine having a cold and you want to remedy the cold by using natural sources. Cold ends up lasting a few days you buy over the counter medicine next time you get a cold and it lasts a week will you take a survey with your friends and realize that they all took a homeopathic medicine that lasts only less days then the treatment you took. The ANOVA lies more of an example is that two groups drink is using tea as a example green tea, black tea or no tea the purpose is to see the effects on on weight less.